Logistic regression approach to predict the likelihood of having a breast cancer using Wisconsin breast cancer dataset from UCI machine learning repository.

Logistic regression approach to predict the likelihood of having a breast cancer using Wisconsin breast cancer dataset from UCI machine learning repository.

The purpose of this analysis is, forecasting bitcoin price based on time series model. Bitcoin is the very first cryptocurrency found in 2009. There are many arguments still prevail in financial market how to value a cryptocurrency as it behaves like a fiat currency and a asset. In this analysis I try to find a reasonable time series model by analysing historical behaviour of the currency. The techniques used here are Auto-Regressive Moving Average (ARIMA) and Generalised Auto-Regressive Conditional Heteroskedasticity (GARCH) in finding the best model that fits bitcoin prices. We use historical daily closed prices of the market for 27/04/2013 to 24/02/2019 published by coinmarketcap.com.

A machine learning approach to predict a person diagnoses a heart disease based on Cleveland data set from UCI machine learning repository.

Descriptive Analysis